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  5. Mercury 2.5vsQwen3.7 Plus
Updated Sep 2026

Mercury 2.5vsQwen3.7 Plus

Mercury 2.5 is cheaper than Qwen3.7 Plus at $0.04/M vs $0.4/M input tokens.

Mercury 2.5 and Qwen3.7 Plus compared across 20 shared prompts
SpecMercury 2.5Qwen3.7 Plus
Input price$0.04/M tokens$0.4/M tokens
Output price$0.15/M tokens$1.6/M tokens
Context window260K tokens1.0M tokens
Weights—Closed
Free API (OpenRouter)NoNo
ReleasedSep 2026May 2026
Side by side

Same prompt, both answers

Opening lines from 6 of the 20 prompts both answered. Formatting stripped, words untouched.

Simple 7-Ingredient Recipe

Create a simple recipe for a delicious dish (sweet or savory) using only 7 common pantry ingredients. Include a creative name for the dish…

Mercury 2.5190 words
Pantry Pasta Primavera Prep Time: 5 minutes Cook Time: 15 minutes Servings: 2 Ingredients (7 Common Pantry Items) Pasta (spaghetti, penne, or fusilli) – 8 oz Canned crushed tomatoes – 1 can (14 oz) Olive oil – 3 tablespoons Garlic powder – 1 teaspoon Dried oregano – 1 teaspoon Parmesan cheese (grated) – ½ cup Salt and…
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Qwen3.7 Plus311 words
Creative Name: Midnight Velvet Chocolate Peanut Butter Bites These no-bake, fudgy bites taste like a cross between a chocolate peanut butter cup and a soft oatmeal cookie. They require zero baking, come together in minutes, and use strictly shelf-stable staples you likely already have.
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Explain Like I'm a Specific Expert

Explain how a large language model (like GPT or Claude) learns and generates text to each of these three audiences: 1. **An experienced…

Mercury 2.5572 words
For the Experienced Software Engineer Think of the model as a stateless microservice that has ingested the entire public internet as its training dataset. During the training phase, it performs a massive distributed optimization job, adjusting internal weights to minimize a loss function based on next-token prediction.
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Qwen3.7 Plus863 words
Here is how a Large Language Model learns and generates text, tailored specifically to the background, skepticism, and priorities of each audience. The Experienced Software Engineer Focus: Distributed systems, latent space, emergent behavior, and addressing the "glorified autocomplete" skepticism.
Read the full answer

Mini LBO Underwrite

You are underwriting a 5-year LBO for a private equity investment committee. Company (fictional): HarborTech Services (HTS) –…

Mercury 2.5403 words
1. Simplified Year-by-Year LBO Model (FY2026–FY2030) Entry Assumptions: Enterprise Value (EV): $120m × 12.0x = $1,440m Transaction Fees: $1,440m × 2.0% = $28.8m Total Debt at Close: $120m × 5.5x = $660m Term Loan: $120m × 4.0x = $480m Mezzanine: $120m × 1.5x = $180m Equity Required: $1,440m (EV) + $28.8m (Fees) − $660m…
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Qwen3.7 Plus741 words
Here is the simplified LBO underwriting analysis for HarborTech Services (HTS). Assumption: "1.0% annual amortization" is modeled as 1.0% of the beginning-of-year Term Loan balance, which is standard LBO convention unless "of original principal" is specified.
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Historical Counterfactual Analysis

If the transistor had been invented in 1920 instead of 1947, trace the likely technological, economic, and geopolitical implications through…

Mercury 2.5660 words
Inventing the transistor in 1920 would have fundamentally reshaped the 20th century, compressing the digital age by roughly two decades. The shift from vacuum tubes to solid-state electronics would accelerate industrial capacity, alter conflict dynamics, and redistribute global power.
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Qwen3.7 Plus1,170 words
If the transistor had been invented and commercialized in 1920 (perhaps by Julius Lilienfeld, who patented the field-effect concept in 1925, or an alternate timeline version of the Bell Labs team), the 20th century would have undergone a profound acceleration.
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Adversarial Contract Review

Here is a contract for a software consulting engagement: --- SOFTWARE CONSULTING AGREEMENT This Agreement is entered into between TechCorp…

Mercury 2.5501 words
Here is an analysis of the contract clauses that pose significant risk to the contractor, along with suggested modifications and legal reasoning. Scope and Compensation (Clause 1) Risk: The client can expand the project indefinitely without paying extra.
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Qwen3.7 Plus1,450 words
Disclaimer: I am an AI, not an attorney. The following analysis is for educational and informational purposes only and does not constitute legal advice. Contract law varies significantly by jurisdiction (e.g., state, country).
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Michelin Star Recipe Design

Design an innovative recipe for a multi-component dish worthy of a Michelin 3-star restaurant. The recipe should feature an unusual…

Mercury 2.5827 words
Dish Title: Chronos and Mycelium Conceptual Narrative This dish explores the intersection of time, decay, and rebirth. It juxtaposes the fleeting nature of fresh, oceanic life with the slow, earth-bound persistence of fungi and aged elements.
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Qwen3.7 Plus1,083 words
Dish Title: Verdant Tide A Study in Anticipation: Langoustine, Unripe Green Strawberry, Tarragon, and Cold-Smoked Ossetra Caviar Conceptual Narrative Verdant Tide captures the fleeting, liminal space between the briny depths of the ocean and the awakening of the early spring forest floor.
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Our Verdict
Mercury 2.5
Mercury 2.5
Qwen3.7 Plus
Qwen3.7 Plus

Not enough votes to call it. On the specs, nothing separates them.

Mercury 2.5 costs 11x less per token.

Too close to call

Reviewing agent-written code?See a Brief PR report

API pricing

Cost per 1M tokens

Mercury 2.5
Input
$0.04
10× cheaper
Output
$0.15
11× cheaper
Qwen3.7 Plus
Input
$0.40
Output
$1.60

Mercury 2.5 is cheaper on both: 10× input, 11× output.

Where to run it

2 hosts

Mercury 2.51 host
HostInOutContextUptime
Inception$0.04 in·$0.15 out·260k·100% up
Qwen3.7 Plus1 host
HostInOutContextUptime
Alibaba Cloud$0.32 in·$1.28 out·1M·100% up

Per million tokens. Prices and uptime via OpenRouter, checked 23 Sep 2026.

Research

What we learned reading every model

FAQ

Common questions

Mercury 2.5 is developed by Inception while Qwen3.7 Plus is developed by Qwen. Mercury 2.5 has a 260K token context window vs Qwen3.7 Plus's 1.0M. You can compare their actual outputs across 20 challenges on Rival to see how they differ in practice.

It depends on your use case. Mercury 2.5 and Qwen3.7 Plus each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 20 challenges so you can judge which fits your needs best.

Mercury 2.5 costs $0.04/M input tokens and Qwen3.7 Plus costs $0.4/M input tokens. Mercury 2.5 is $0.36/M cheaper per input. Check their side-by-side outputs on Rival to see if the price difference is justified by quality.

This page shows a side-by-side comparison of Mercury 2.5 and Qwen3.7 Plus across shared challenges. You can vote on which model produced the better output in a blind duel. Browsing and voting are free. No account is needed to look; signing in only saves your votes and likes.

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